Marsh decrease was much faster than the water increase among the Yellow River Source wetlands during 1986-2022.

Wetlands are valuable and sensitive ecosystems that make them imperative to tracking the dynamics in their extent for sustainable management under global warming. Here we focused on the Yellow River Source (YRS) wetlands, which is renowned for hosting one of the world's largest plateau peat bog, unfortunately, it had experienced sharp degradation, threatening the safety of water supply for approximately 110 million people of the lower Yellow River basin. However, the lack of long-term, dense time-series data makes it challenging to assess its evolution trends and driving factors. Therefore, we developed a decision tree sample migration method based on Euclidean distance and Land Surface Water Index, and successfully generated annual wetland mapping of YRS from 1986 to 2022 by utilizing the Landsat 5/7/8 datasets and Random Forest method. The average sample migration rate was 89.21 %, with an average overall accuracy of 95.49 %. We observed that the marsh area decreased by 2031 km2, marking a decline of 12.98 %, while the water area increased by 710 km2 (31.24 %) compared to 1986. Spatially, 10.96 % of marsh composition presents significant (P < 0.05) decline trend, which are mainly converted to grass (86 %), followed by impervious (10 %). There were 6.69 % of water composition showing significant (P < 0.05) increase trend, which are mainly sourced from impervious (82 %) and marsh (12 %). Grazing activities were more important driving forces than climate change for marsh degradation, while the water expansion was associated with recent rising temperature in YRS. The sample migration method is proved to be feasible, robust, and effective for long-term wetland mapping. We suggest that wetland decision-makers need to focus on marsh degradation and reduce grazing intensity, so that fostering the sustainable and healthy wetlands in the Qinghai-Tibetan Plateau.

Electrochemiluminescence resonance energy transfer immunoassay based on a porphyrin metal-organic framework and AuNPs/NSG for the sensitive detection of zearalenone.

In this study, a novel electrochemiluminescence resonance energy transfer (ECL-RET) immunoassay was developed for the first time for the detection of zearalenone (ZEN). A porphyrin metal-organic framework (PCN-222), an emerging porphyrin-based ECL luminophore, was prepared by a simple hydrothermal method using tetrakis(4-carboxyphenyl) porphyrin, which has excellent ECL emission as well as good ECL efficiency. Because the ECL emission spectrum of PCN-222 is highly matched to the absorption spectrum of gold nanoparticle-modified graphene oxide (AuNPs/NSG) nanocomposites, they were used as donor-acceptor counterparts in this work for the ECL-RET strategy. Under optimal conditions, the ECL immunosensor showed a sensitive response to ZEN in a wide detection range, with a linearity of 0.0005-1000 ng mL-1 and a detection limit of 0.15 pg mL-1. In addition, the sensor showed good potential for application in the detection of wheat and corn samples, providing a new approach for the detection of mycotoxin-like contaminants such as ZEN in food grains.

Identifying the influencing factors and constructing incentive pattern of residents' waste classification behavior using PCA-logistic regression.

With the acceleration of urbanization, domestic waste has become one of the most inevitable factors threatening the environment and human health. Waste classification is of great significance and value for improving urban environmental quality and promoting human well-being. Based on the theory of planned behavior, we added external and socio-economic factors to systematically examine how they affect residents' waste classification behavior (WCB). We collected 661 valid data through a questionnaire survey conducted in Jinan, a pilot city for waste classification in China. Key driving factors were identified by combining binary logistic regression and the principal component analysis. The results showed that the elderly, women, and people with higher education are more likely to participate in waste classification. Attitude, collaborative governance, and institutional pressure positively affect WCB, while subjective norm and infrastructure have a negative effect. Knowledge mastery and degree of publicity are positively and significantly related to WCB, but other perceived behavioral control sub-variables negatively affect WCB. Based on the results and status of waste classification in Jinan, we propose the multi-agent linkage governance pattern from various dimensions to explore a powerful guiding incentive that can enhance WCB and provide a reference for waste management policymakers.

Hydroxysafflor Yellow A Phytosomes Administered via Intervaginal Space Injection Ameliorate Pulmonary Fibrosis in Mice.

Idiopathic pulmonary fibrosis is a fatal interstitial disease characterized by fibroblast proliferation and differentiation and abnormal accumulation of extracellular matrix, with high mortality and an increasing annual incidence. Since few drugs are available for the treatment of pulmonary fibrosis, there is an urgent need for high-efficiency therapeutic drugs and treatment methods to reduce the mortality associated with pulmonary fibrosis. The interstitium, a highly efficient transportation system that pervades the body, plays an important role in the occurrence and development of disease, and can be used as a new route for disease diagnosis and treatment. In this study, we evaluated the administration of hydroxysafflor yellow A phytosomes via intervaginal space injection (ISI) as an anti-pulmonary fibrosis treatment. Our results show that this therapeutic strategy blocked the activation of p38 protein in the MAPK-p38 signaling pathway and inhibited the expression of Smad3 protein in the TGF-ß/Smad signaling pathway, thereby reducing secretion of related inflammatory factors, deposition of collagen in the lungs of mice, and destruction of the alveolar structure. Use of ISI in the treatment of pulmonary fibrosis provides a potential novel therapeutic modality for the disease.

Peripheral blood inflammatory markers in predicting prognosis in patients with COVID-19. Some differences with influenza A.

BACKGROUND: To evaluate the ability of peripheral blood inflammatory markers in predicating the typing of COVID-19, prognosis, and some differences between COVID-19 and influenza A patients. METHODS: Clinical data on 285 cases laboratory-confirmed as SARS-CoV-2 infection were obtained from a Wuhan local hospital's electronic medical records according to previously designed standardized data collection forms. Additional 446 Influenza A outpatients' hematologic data were enrolled for comparison. RESULTS: NLR, SII, RLR, PLR, HsCRP, and IL-6 were significant higher and LMR was lower in severe COVID-19 patients than in mild COVID-19 patients (p < .001). PLR and LMR were lower in the individuals with influenza A than those with COVID-19 (p < .01). COVID-19 patients with higher levels of NLR, SII, RLR, PLR, HsCRP, and IL-6 and lower LMR were significantly associated with the severe type. AUC of NLR (0.76) was larger while the specificity of IL-6 (86%) and sensitivity of HsCRP (89%) were higher than other inflammatory markers in predicating the typing of COVID-19. PT had obvious correlation with all the inflammatory markers except RPR. NLR showed positive correlations with AST, TP, BUN, CREA, PT, and D-dimer. Patients with high IL-6 levels have a relatively worse prognosis (HR = 2.30). CONCLUSION: Peripheral blood inflammatory markers reflected the intensity of inflammation and associated with severity of COVID-19.NLR was more useful to predict severity as well as IL-6 to predict prognosis of COVID-19. PLR and LMR were initially found to be higher in SARS-CoV-2 virus-infected group than in influenza A.